Accuracy Matters
In finance, accuracy is non-negotiable. From ensuring compliance to managing complex portfolios, decisions are only as good as the data they’re based on. Artificial intelligence (AI) offers the potential to transform financial operations, but the effectiveness of any AI system depends on one critical factor: the accuracy and consistency of its data inputs.
I once spoke with a fund administrator who was excited to implement an AI-driven risk management tool. The system promised real-time insights into portfolio exposure and potential vulnerabilities. But within weeks, they began to see false positives and overlooked risks. The issue wasn’t with the AI itself—it was with the data. Fragmented systems and inconsistent reporting created gaps that the AI couldn’t bridge.
AI amplifies the patterns and insights found in the data it’s fed. If that data is fragmented, incomplete, or outdated, the outputs will be flawed. In finance, where even small errors can cascade into significant issues, this simply isn’t acceptable.
Key Takeaway:
- Fragmented or inconsistent data leads to flawed predictions, undermining AI’s value in critical financial tasks like risk assessment and portfolio optimization.
- Tools like unified general ledgers consolidate data into a central, consistent framework, ensuring accuracy and reducing manual intervention.
- Reliable data foundations allow AI to support real-time decision-making, ensure compliance, and enhance transparency in financial operations.
AI Stumbles if You Feed it Junk
AI models are like engines, and data is their fuel. Just as you wouldn’t expect a car to perform well on contaminated fuel, you can’t expect AI to deliver reliable results with messy data. When financial professionals rely on fragmented or outdated datasets, AI outputs often become unreliable.
Take portfolio optimization, for example. AI algorithms need precise, up-to-date data on asset performance, risk factors, and market conditions. If a single source of truth isn’t in place, discrepancies between datasets—such as mismatched valuations or missing transactions—can lead to flawed recommendations. A decision based on such outputs isn’t just suboptimal; it’s potentially disastrous.
According to a 2023 Gartner study, poor data quality costs organizations an average of $12.9 million annually, impacting everything from decision-making to compliance. In finance, where margins are tight and stakes are high, the risks are even greater.
Why Data Becomes Fragmented
In many financial organizations, data lives in silos. Trading platforms, accounting systems, and operational tools each manage their own slice of the picture, but they rarely communicate seamlessly. This fragmentation leads to discrepancies—different versions of the same data that require manual reconciliation.
For instance, a portfolio might have slightly different valuations across systems, or transactions logged in one platform may not appear in another. AI cannot effectively process such mismatched data, and the result is unreliable outputs. The problem isn’t the AI—it’s the lack of a cohesive, accurate data foundation.
Achieving accuracy requires integrating all financial data into a single, consistent framework. When this is done effectively, it results in what’s known as a single source of truth (SSOT)—a reliable dataset that eliminates fragmentation and ensures every input is accurate and up to date.
Unified General Ledger does an End Around the Confusion
FundCount tackles the problem of fragmented and inconsistent data by placing a unified general ledger at the core of its system. Unlike traditional multi-ledger supported tech stacks, FundCount consolidates all financial transactions into a single, centralized system. This design ensures that updates flow seamlessly across accounts, eliminating discrepancies before they arise and providing a clear, consistent picture of financial activity.
The unified general ledger records every transaction once, capturing all relevant details and making the data instantly accessible across reporting tools. This structure removes the need for manual reconciliations, which are not only time-consuming but prone to errors. For financial professionals, this means reports and insights drawn from the system are based on clean, accurate data—ready to be trusted and acted upon.
A unified general ledger addresses the challenges of fragmented and inconsistent data by consolidating all financial transactions into a single, centralized framework. This ensures that every transaction is recorded once and made accessible across the entire system, reducing the need for manual reconciliations and eliminating discrepancies between different data sources.
With all financial data flowing through one ledger, reports and analyses are generated from a consistent and integrated dataset. This creates a single source of truth, where the data feeding into AI systems is complete, accurate, and up to date. By removing the inefficiencies and risks associated with siloed systems, a unified general ledger provides the foundation needed for AI to generate reliable, actionable insights in complex financial environments.
Gaining an Edge with SSOT
A single source of truth consolidates all financial data into one reliable, centralized system. It’s not just a technical solution; it’s a mindset shift that puts accuracy at the center of financial operations.
Here’s how it works in practice:
- Real-Time Accuracy: Imagine managing alternative assets like crypto, which require extended decimal precision for accurate reporting. A single source of truth ensures that all transactions are logged consistently, avoiding the kind of rounding errors that can skew performance data. This is particularly critical for AI-driven systems tasked with real-time risk analysis or portfolio rebalancing.
- Eliminating the Noise: A unified system removes redundancies and inconsistencies. One firm I worked with used to spend weeks reconciling spreadsheets before running AI models. By switching to a single source of truth, they eliminated manual reconciliations, allowing their AI to focus on insights rather than correcting mistakes.
- Transparency for Trust: In finance, decisions don’t just need to be accurate—they need to be explainable. With a single source of truth, you can trace every AI recommendation back to the raw data, providing confidence to stakeholders, auditors, and clients alike.
Case in Point: Data Integrity in Fund Administration
Consider fund administrators who handle complex multi-entity portfolios. Without a single source of truth, these administrators often rely on siloed systems to track transactions, leading to mismatched records and missed insights. One firm I worked with discovered discrepancies between their trading platform and accounting software that were costing them hours in manual corrections.
By implementing a unified general ledger, they not only ensured accurate data flow across their systems but also enabled their AI tools to detect patterns in fund performance and risk exposure. The result? Faster reporting, more reliable insights, and greater trust from their investors.
Accuracy is the Key to AI Success
AI’s promise in the financial industry is immense, but its success hinges on a foundation of accurate data. Without a single source of truth that provides that foundation, there is no ensuring data is clean, consistent, or accessible in real time. For financial professionals, this isn’t just about technology—it’s about creating a system that delivers reliable insights, reduces risk, and ultimately builds trust.
As the industry moves further into AI-driven solutions, firms that prioritize accuracy will have the edge. Because when it comes to AI, the old saying holds true: garbage in, garbage out.
